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Deep learning-based initial guess for minimum energy path calculations

  • Separation Technology, Thermodynamics
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Abstract

An autoencoder that automatically generates an initial guess for the minimum energy pathway (MEP) calculations has been designed. Specifically, our autoencoder takes in the trajectories of molecular dynamics simulations as its input and facilitates the generation of feasible molecular coordinates. Two molecules (acetonitrile and alanine dipeptide) were tested using the nudged elastic band calculations and the results provided improvements over linear interpolation and image dependent pair potential methods in terms of the number of SCF iterations, demonstrating the utility of using an autoencoder type of an approach for MEP calculations.

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Acknowledgement

This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-MA1702-07.

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Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea

    Hyunsoo Park, Sangwon Lee & Jihan Kim

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  1. Hyunsoo Park
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  2. Sangwon Lee
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  3. Jihan Kim
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Correspondence to Jihan Kim.

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Park, H., Lee, S. & Kim, J. Deep learning-based initial guess for minimum energy path calculations. Korean J. Chem. Eng. 38, 406–410 (2021). https://doi.org/10.1007/s11814-020-0704-1

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  • DOI: https://doi.org/10.1007/s11814-020-0704-1

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